NDLI: Proceedings of the 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '14)

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Scaling parallel 3-D FFT with non-blocking MPI collectives

Exploiting data representation for fault tolerance

VCube: a provably scalable distributed diagnosis algorithm

TX: algorithmic energy saving for distributed dense matrix factorizations

Workshop Overview

CholeskyQR2: a simple and communication-avoiding algorithm for computing a tall-skinny QR factorization on a large-scale parallel system

ScalA'14 Foreword

Deflation strategies to improve the convergence of communication-avoiding GMRES

A framework for parallel genetic algorithms for distributed memory architectures

The anatomy of Mr. Scan: a dissection of performance of an extreme scale GPU-based clustering algorithm

Performance and portability with OpenCL for throughput-oriented HPC workloads across accelerators, coprocessors, and multicore processors

A hierarchical tridiagonal system solver for heterogenous supercomputers

Proceedings of the 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '14)

Content Provider	ACM Digital Library
Editor	Engelmann, Christian Alexandrov, Vassil Geist, Al
Copyright Year	2014
Abstract	Novel scalable scientific algorithms are needed in order to enable key science applications to exploit the computational power of large-scale systems. This is especially true for the current tier of leading petascale machines and the road to exascale computing as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, and have no synchronization points. Scientific algorithms for multi-petaflop and exa-flop systems also need to be fault tolerant and fault resilient, since the probability of faults increases with scale. Resilience at the system software and at the algorithmic level is needed as a crosscutting effort. Finally, with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs, scientific algorithms need to match these architectures to extract the most performance. This includes different system-specific levels of parallelism as well as co-scheduling of computation. Key science applications require novel mathematical models and system software that address the scalability and resilience challenges of current- and future-generation extreme-scale HPC systems. The goal of this workshop is to bring together experts in the area of scalable algorithms to present the latest achievements and to discuss the challenges ahead.
Related Links	http://computer.org/proceedings/scala/2014/7562
ISBN	9781479975624
Language	English
Publisher	IEEE Press
Publisher Date	2014-11-16
Access Restriction	Subscribed
Content Type	Text
Resource Type	Conference Proceedings

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